Analysis of 111/999 NHS calls and 111 online reports
Quentin Leclerc, Emily Nightingale, Samuel Abbott & Thibaut Jombart, for CMMID
Saturday 20 June 2020
1 Data preparation
1.1 Outline
Load scripts: loads libraries and useful scripts used in the analyses; all
.Rfiles contained inscriptsat the root of the factory are automatically loadedLoad data: imports datasets, and may contain some ad hoc changes to the data such as specific data cleaning (not used in other reports), new variables used in the analyses, etc.
1.2 Load packages
library(reportfactory)
library(here)
library(rio)
library(tidyverse)
library(incidence)
library(distcrete)
library(epitrix)
library(earlyR)
library(projections)
library(linelist)
library(remotes)
library(janitor)
library(kableExtra)
library(DT)
library(cyphr)
library(chngpt)
library(lubridate)
library(ggpubr)
library(ggnewscale)1.3 Load scripts
These scripts will load:
- all scripts stored as
.Rfiles inside/scripts/ - all scripts stored as
.Rfiles inside/src/
These scripts also contain routines to access the latest clean encrypted data (see next section).
1.4 Load clean data
We import the latest NHS pathways data:
x <- import_pathways() %>%
as_tibble()
x
## [90m# A tibble: 150,688 x 11[39m
## site_type date sex age ccg_code ccg_name count postcode nhs_region
## [3m[90m<chr>[39m[23m [3m[90m<date>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<int>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m
## [90m 1[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bar… 35 rm13ae London
## [90m 2[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bed… 27 mk454hr East of E…
## [90m 3[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bla… 9 bb12fd North West
## [90m 4[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bro… 11 br33ql London
## [90m 5[39m 111 2020-03-18 fema… 0-18 e380000… nhs_can… 9 ws111jp Midlands
## [90m 6[39m 111 2020-03-18 fema… 0-18 e380000… nhs_cit… 12 n15lz London
## [90m 7[39m 111 2020-03-18 fema… 0-18 e380000… nhs_enf… 7 en40dy London
## [90m 8[39m 111 2020-03-18 fema… 0-18 e380000… nhs_ham… 6 dl62uu North Eas…
## [90m 9[39m 111 2020-03-18 fema… 0-18 e380000… nhs_har… 24 ts232la North Eas…
## [90m10[39m 111 2020-03-18 fema… 0-18 e380000… nhs_kin… 6 kt11eu London
## [90m# … with 150,678 more rows, and 2 more variables: day [3m[90m<int>[90m[23m, weekday [3m[90m<fct>[90m[23m[39mWe also import demographics data for NHS regions in England, used later in our analysis:
path <- here::here("data", "csv", "nhs_region_population_2018.csv")
nhs_region_pop <- rio::import(path) %>%
mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))
nhs_region_pop$nhs_region <- gsub(" Of ", " of ", nhs_region_pop$nhs_region)
nhs_region_pop$nhs_region <- gsub(" And ", " and ", nhs_region_pop$nhs_region)
nhs_region_pop
## nhs_region variable value
## 1 North West 0-18 0.22538599
## 2 North East and Yorkshire 0-18 0.21876449
## 3 Midlands 0-18 0.22564656
## 4 East of England 0-18 0.22810783
## 5 London 0-18 0.23764782
## 6 South East 0-18 0.22458811
## 7 South West 0-18 0.20799797
## 8 North West 19-69 0.64274078
## 9 North East and Yorkshire 19-69 0.64437753
## 10 Midlands 19-69 0.63876675
## 11 East of England 19-69 0.63034229
## 12 London 19-69 0.67820084
## 13 South East 19-69 0.63267336
## 14 South West 19-69 0.63176131
## 15 North West 70-120 0.13187323
## 16 North East and Yorkshire 70-120 0.13685797
## 17 Midlands 70-120 0.13558669
## 18 East of England 70-120 0.14154988
## 19 London 70-120 0.08415135
## 20 South East 70-120 0.14273853
## 21 South West 70-120 0.16024072Finally, we import publically available deaths per NHS region:
dth <- import_deaths() %>%
mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))
#truncation to account for reporting delay
delay_max <- 21
dth$nhs_region <- gsub(" Of ", " of ", dth$nhs_region)
dth$nhs_region <- gsub(" And ", " and ", dth$nhs_region)
dth
## date_report nhs_region deaths
## 1 2020-03-01 East of England 0
## 2 2020-03-02 East of England 1
## 3 2020-03-03 East of England 0
## 4 2020-03-04 East of England 0
## 5 2020-03-05 East of England 0
## 6 2020-03-06 East of England 1
## 7 2020-03-07 East of England 0
## 8 2020-03-08 East of England 0
## 9 2020-03-09 East of England 1
## 10 2020-03-10 East of England 0
## 11 2020-03-11 East of England 0
## 12 2020-03-12 East of England 0
## 13 2020-03-13 East of England 1
## 14 2020-03-14 East of England 2
## 15 2020-03-15 East of England 2
## 16 2020-03-16 East of England 1
## 17 2020-03-17 East of England 1
## 18 2020-03-18 East of England 5
## 19 2020-03-19 East of England 4
## 20 2020-03-20 East of England 2
## 21 2020-03-21 East of England 11
## 22 2020-03-22 East of England 12
## 23 2020-03-23 East of England 11
## 24 2020-03-24 East of England 19
## 25 2020-03-25 East of England 26
## 26 2020-03-26 East of England 36
## 27 2020-03-27 East of England 38
## 28 2020-03-28 East of England 28
## 29 2020-03-29 East of England 43
## 30 2020-03-30 East of England 45
## 31 2020-03-31 East of England 70
## 32 2020-04-01 East of England 62
## 33 2020-04-02 East of England 64
## 34 2020-04-03 East of England 80
## 35 2020-04-04 East of England 71
## 36 2020-04-05 East of England 76
## 37 2020-04-06 East of England 71
## 38 2020-04-07 East of England 93
## 39 2020-04-08 East of England 111
## 40 2020-04-09 East of England 87
## 41 2020-04-10 East of England 74
## 42 2020-04-11 East of England 91
## 43 2020-04-12 East of England 101
## 44 2020-04-13 East of England 78
## 45 2020-04-14 East of England 61
## 46 2020-04-15 East of England 82
## 47 2020-04-16 East of England 74
## 48 2020-04-17 East of England 86
## 49 2020-04-18 East of England 64
## 50 2020-04-19 East of England 67
## 51 2020-04-20 East of England 67
## 52 2020-04-21 East of England 75
## 53 2020-04-22 East of England 67
## 54 2020-04-23 East of England 49
## 55 2020-04-24 East of England 66
## 56 2020-04-25 East of England 54
## 57 2020-04-26 East of England 48
## 58 2020-04-27 East of England 46
## 59 2020-04-28 East of England 58
## 60 2020-04-29 East of England 32
## 61 2020-04-30 East of England 45
## 62 2020-05-01 East of England 49
## 63 2020-05-02 East of England 29
## 64 2020-05-03 East of England 41
## 65 2020-05-04 East of England 19
## 66 2020-05-05 East of England 36
## 67 2020-05-06 East of England 31
## 68 2020-05-07 East of England 33
## 69 2020-05-08 East of England 33
## 70 2020-05-09 East of England 29
## 71 2020-05-10 East of England 22
## 72 2020-05-11 East of England 18
## 73 2020-05-12 East of England 21
## 74 2020-05-13 East of England 27
## 75 2020-05-14 East of England 26
## 76 2020-05-15 East of England 19
## 77 2020-05-16 East of England 26
## 78 2020-05-17 East of England 17
## 79 2020-05-18 East of England 25
## 80 2020-05-19 East of England 15
## 81 2020-05-20 East of England 26
## 82 2020-05-21 East of England 21
## 83 2020-05-22 East of England 13
## 84 2020-05-23 East of England 12
## 85 2020-05-24 East of England 17
## 86 2020-05-25 East of England 25
## 87 2020-05-26 East of England 14
## 88 2020-05-27 East of England 12
## 89 2020-05-28 East of England 17
## 90 2020-05-29 East of England 16
## 91 2020-05-30 East of England 9
## 92 2020-05-31 East of England 8
## 93 2020-06-01 East of England 17
## 94 2020-06-02 East of England 14
## 95 2020-06-03 East of England 10
## 96 2020-06-04 East of England 7
## 97 2020-06-05 East of England 12
## 98 2020-06-06 East of England 5
## 99 2020-06-07 East of England 9
## 100 2020-06-08 East of England 5
## 101 2020-06-09 East of England 6
## 102 2020-06-10 East of England 8
## 103 2020-06-11 East of England 0
## 104 2020-06-12 East of England 9
## 105 2020-06-13 East of England 5
## 106 2020-06-14 East of England 4
## 107 2020-06-15 East of England 6
## 108 2020-06-16 East of England 2
## 109 2020-06-17 East of England 4
## 110 2020-06-18 East of England 1
## 111 2020-03-01 London 0
## 112 2020-03-02 London 0
## 113 2020-03-03 London 0
## 114 2020-03-04 London 0
## 115 2020-03-05 London 0
## 116 2020-03-06 London 1
## 117 2020-03-07 London 0
## 118 2020-03-08 London 0
## 119 2020-03-09 London 1
## 120 2020-03-10 London 0
## 121 2020-03-11 London 6
## 122 2020-03-12 London 6
## 123 2020-03-13 London 10
## 124 2020-03-14 London 14
## 125 2020-03-15 London 10
## 126 2020-03-16 London 15
## 127 2020-03-17 London 23
## 128 2020-03-18 London 27
## 129 2020-03-19 London 25
## 130 2020-03-20 London 44
## 131 2020-03-21 London 49
## 132 2020-03-22 London 54
## 133 2020-03-23 London 63
## 134 2020-03-24 London 87
## 135 2020-03-25 London 113
## 136 2020-03-26 London 129
## 137 2020-03-27 London 130
## 138 2020-03-28 London 122
## 139 2020-03-29 London 146
## 140 2020-03-30 London 149
## 141 2020-03-31 London 181
## 142 2020-04-01 London 202
## 143 2020-04-02 London 190
## 144 2020-04-03 London 196
## 145 2020-04-04 London 230
## 146 2020-04-05 London 195
## 147 2020-04-06 London 197
## 148 2020-04-07 London 220
## 149 2020-04-08 London 238
## 150 2020-04-09 London 206
## 151 2020-04-10 London 170
## 152 2020-04-11 London 177
## 153 2020-04-12 London 158
## 154 2020-04-13 London 166
## 155 2020-04-14 London 144
## 156 2020-04-15 London 142
## 157 2020-04-16 London 139
## 158 2020-04-17 London 100
## 159 2020-04-18 London 101
## 160 2020-04-19 London 103
## 161 2020-04-20 London 95
## 162 2020-04-21 London 94
## 163 2020-04-22 London 109
## 164 2020-04-23 London 77
## 165 2020-04-24 London 71
## 166 2020-04-25 London 58
## 167 2020-04-26 London 53
## 168 2020-04-27 London 51
## 169 2020-04-28 London 43
## 170 2020-04-29 London 44
## 171 2020-04-30 London 40
## 172 2020-05-01 London 41
## 173 2020-05-02 London 40
## 174 2020-05-03 London 36
## 175 2020-05-04 London 30
## 176 2020-05-05 London 25
## 177 2020-05-06 London 37
## 178 2020-05-07 London 37
## 179 2020-05-08 London 30
## 180 2020-05-09 London 23
## 181 2020-05-10 London 26
## 182 2020-05-11 London 18
## 183 2020-05-12 London 18
## 184 2020-05-13 London 16
## 185 2020-05-14 London 20
## 186 2020-05-15 London 18
## 187 2020-05-16 London 14
## 188 2020-05-17 London 15
## 189 2020-05-18 London 9
## 190 2020-05-19 London 14
## 191 2020-05-20 London 19
## 192 2020-05-21 London 12
## 193 2020-05-22 London 10
## 194 2020-05-23 London 6
## 195 2020-05-24 London 7
## 196 2020-05-25 London 9
## 197 2020-05-26 London 12
## 198 2020-05-27 London 7
## 199 2020-05-28 London 8
## 200 2020-05-29 London 7
## 201 2020-05-30 London 12
## 202 2020-05-31 London 6
## 203 2020-06-01 London 10
## 204 2020-06-02 London 7
## 205 2020-06-03 London 6
## 206 2020-06-04 London 8
## 207 2020-06-05 London 4
## 208 2020-06-06 London 0
## 209 2020-06-07 London 4
## 210 2020-06-08 London 5
## 211 2020-06-09 London 2
## 212 2020-06-10 London 7
## 213 2020-06-11 London 5
## 214 2020-06-12 London 3
## 215 2020-06-13 London 3
## 216 2020-06-14 London 2
## 217 2020-06-15 London 1
## 218 2020-06-16 London 2
## 219 2020-06-17 London 1
## 220 2020-06-18 London 0
## 221 2020-03-01 Midlands 0
## 222 2020-03-02 Midlands 0
## 223 2020-03-03 Midlands 1
## 224 2020-03-04 Midlands 0
## 225 2020-03-05 Midlands 0
## 226 2020-03-06 Midlands 0
## 227 2020-03-07 Midlands 0
## 228 2020-03-08 Midlands 3
## 229 2020-03-09 Midlands 1
## 230 2020-03-10 Midlands 0
## 231 2020-03-11 Midlands 2
## 232 2020-03-12 Midlands 6
## 233 2020-03-13 Midlands 5
## 234 2020-03-14 Midlands 4
## 235 2020-03-15 Midlands 5
## 236 2020-03-16 Midlands 11
## 237 2020-03-17 Midlands 8
## 238 2020-03-18 Midlands 13
## 239 2020-03-19 Midlands 8
## 240 2020-03-20 Midlands 28
## 241 2020-03-21 Midlands 13
## 242 2020-03-22 Midlands 31
## 243 2020-03-23 Midlands 33
## 244 2020-03-24 Midlands 41
## 245 2020-03-25 Midlands 48
## 246 2020-03-26 Midlands 64
## 247 2020-03-27 Midlands 72
## 248 2020-03-28 Midlands 89
## 249 2020-03-29 Midlands 92
## 250 2020-03-30 Midlands 90
## 251 2020-03-31 Midlands 123
## 252 2020-04-01 Midlands 140
## 253 2020-04-02 Midlands 142
## 254 2020-04-03 Midlands 124
## 255 2020-04-04 Midlands 151
## 256 2020-04-05 Midlands 164
## 257 2020-04-06 Midlands 140
## 258 2020-04-07 Midlands 123
## 259 2020-04-08 Midlands 186
## 260 2020-04-09 Midlands 139
## 261 2020-04-10 Midlands 127
## 262 2020-04-11 Midlands 142
## 263 2020-04-12 Midlands 139
## 264 2020-04-13 Midlands 120
## 265 2020-04-14 Midlands 116
## 266 2020-04-15 Midlands 147
## 267 2020-04-16 Midlands 102
## 268 2020-04-17 Midlands 118
## 269 2020-04-18 Midlands 115
## 270 2020-04-19 Midlands 92
## 271 2020-04-20 Midlands 107
## 272 2020-04-21 Midlands 86
## 273 2020-04-22 Midlands 78
## 274 2020-04-23 Midlands 103
## 275 2020-04-24 Midlands 79
## 276 2020-04-25 Midlands 72
## 277 2020-04-26 Midlands 81
## 278 2020-04-27 Midlands 74
## 279 2020-04-28 Midlands 68
## 280 2020-04-29 Midlands 53
## 281 2020-04-30 Midlands 56
## 282 2020-05-01 Midlands 64
## 283 2020-05-02 Midlands 51
## 284 2020-05-03 Midlands 52
## 285 2020-05-04 Midlands 61
## 286 2020-05-05 Midlands 58
## 287 2020-05-06 Midlands 59
## 288 2020-05-07 Midlands 48
## 289 2020-05-08 Midlands 34
## 290 2020-05-09 Midlands 37
## 291 2020-05-10 Midlands 42
## 292 2020-05-11 Midlands 33
## 293 2020-05-12 Midlands 45
## 294 2020-05-13 Midlands 40
## 295 2020-05-14 Midlands 37
## 296 2020-05-15 Midlands 40
## 297 2020-05-16 Midlands 34
## 298 2020-05-17 Midlands 31
## 299 2020-05-18 Midlands 34
## 300 2020-05-19 Midlands 34
## 301 2020-05-20 Midlands 36
## 302 2020-05-21 Midlands 32
## 303 2020-05-22 Midlands 27
## 304 2020-05-23 Midlands 34
## 305 2020-05-24 Midlands 19
## 306 2020-05-25 Midlands 26
## 307 2020-05-26 Midlands 33
## 308 2020-05-27 Midlands 29
## 309 2020-05-28 Midlands 27
## 310 2020-05-29 Midlands 20
## 311 2020-05-30 Midlands 20
## 312 2020-05-31 Midlands 22
## 313 2020-06-01 Midlands 20
## 314 2020-06-02 Midlands 22
## 315 2020-06-03 Midlands 24
## 316 2020-06-04 Midlands 15
## 317 2020-06-05 Midlands 21
## 318 2020-06-06 Midlands 20
## 319 2020-06-07 Midlands 16
## 320 2020-06-08 Midlands 15
## 321 2020-06-09 Midlands 17
## 322 2020-06-10 Midlands 14
## 323 2020-06-11 Midlands 13
## 324 2020-06-12 Midlands 12
## 325 2020-06-13 Midlands 6
## 326 2020-06-14 Midlands 16
## 327 2020-06-15 Midlands 11
## 328 2020-06-16 Midlands 10
## 329 2020-06-17 Midlands 7
## 330 2020-06-18 Midlands 0
## 331 2020-03-01 North East and Yorkshire 0
## 332 2020-03-02 North East and Yorkshire 0
## 333 2020-03-03 North East and Yorkshire 0
## 334 2020-03-04 North East and Yorkshire 0
## 335 2020-03-05 North East and Yorkshire 0
## 336 2020-03-06 North East and Yorkshire 0
## 337 2020-03-07 North East and Yorkshire 0
## 338 2020-03-08 North East and Yorkshire 0
## 339 2020-03-09 North East and Yorkshire 0
## 340 2020-03-10 North East and Yorkshire 0
## 341 2020-03-11 North East and Yorkshire 0
## 342 2020-03-12 North East and Yorkshire 0
## 343 2020-03-13 North East and Yorkshire 0
## 344 2020-03-14 North East and Yorkshire 0
## 345 2020-03-15 North East and Yorkshire 2
## 346 2020-03-16 North East and Yorkshire 3
## 347 2020-03-17 North East and Yorkshire 1
## 348 2020-03-18 North East and Yorkshire 2
## 349 2020-03-19 North East and Yorkshire 6
## 350 2020-03-20 North East and Yorkshire 5
## 351 2020-03-21 North East and Yorkshire 6
## 352 2020-03-22 North East and Yorkshire 7
## 353 2020-03-23 North East and Yorkshire 9
## 354 2020-03-24 North East and Yorkshire 8
## 355 2020-03-25 North East and Yorkshire 18
## 356 2020-03-26 North East and Yorkshire 21
## 357 2020-03-27 North East and Yorkshire 28
## 358 2020-03-28 North East and Yorkshire 35
## 359 2020-03-29 North East and Yorkshire 38
## 360 2020-03-30 North East and Yorkshire 64
## 361 2020-03-31 North East and Yorkshire 60
## 362 2020-04-01 North East and Yorkshire 67
## 363 2020-04-02 North East and Yorkshire 74
## 364 2020-04-03 North East and Yorkshire 100
## 365 2020-04-04 North East and Yorkshire 105
## 366 2020-04-05 North East and Yorkshire 92
## 367 2020-04-06 North East and Yorkshire 96
## 368 2020-04-07 North East and Yorkshire 102
## 369 2020-04-08 North East and Yorkshire 107
## 370 2020-04-09 North East and Yorkshire 111
## 371 2020-04-10 North East and Yorkshire 117
## 372 2020-04-11 North East and Yorkshire 98
## 373 2020-04-12 North East and Yorkshire 84
## 374 2020-04-13 North East and Yorkshire 94
## 375 2020-04-14 North East and Yorkshire 107
## 376 2020-04-15 North East and Yorkshire 96
## 377 2020-04-16 North East and Yorkshire 103
## 378 2020-04-17 North East and Yorkshire 88
## 379 2020-04-18 North East and Yorkshire 95
## 380 2020-04-19 North East and Yorkshire 88
## 381 2020-04-20 North East and Yorkshire 100
## 382 2020-04-21 North East and Yorkshire 76
## 383 2020-04-22 North East and Yorkshire 84
## 384 2020-04-23 North East and Yorkshire 63
## 385 2020-04-24 North East and Yorkshire 72
## 386 2020-04-25 North East and Yorkshire 69
## 387 2020-04-26 North East and Yorkshire 65
## 388 2020-04-27 North East and Yorkshire 65
## 389 2020-04-28 North East and Yorkshire 57
## 390 2020-04-29 North East and Yorkshire 69
## 391 2020-04-30 North East and Yorkshire 57
## 392 2020-05-01 North East and Yorkshire 64
## 393 2020-05-02 North East and Yorkshire 48
## 394 2020-05-03 North East and Yorkshire 40
## 395 2020-05-04 North East and Yorkshire 49
## 396 2020-05-05 North East and Yorkshire 40
## 397 2020-05-06 North East and Yorkshire 51
## 398 2020-05-07 North East and Yorkshire 45
## 399 2020-05-08 North East and Yorkshire 42
## 400 2020-05-09 North East and Yorkshire 44
## 401 2020-05-10 North East and Yorkshire 40
## 402 2020-05-11 North East and Yorkshire 29
## 403 2020-05-12 North East and Yorkshire 27
## 404 2020-05-13 North East and Yorkshire 28
## 405 2020-05-14 North East and Yorkshire 30
## 406 2020-05-15 North East and Yorkshire 32
## 407 2020-05-16 North East and Yorkshire 35
## 408 2020-05-17 North East and Yorkshire 26
## 409 2020-05-18 North East and Yorkshire 29
## 410 2020-05-19 North East and Yorkshire 27
## 411 2020-05-20 North East and Yorkshire 21
## 412 2020-05-21 North East and Yorkshire 33
## 413 2020-05-22 North East and Yorkshire 22
## 414 2020-05-23 North East and Yorkshire 18
## 415 2020-05-24 North East and Yorkshire 25
## 416 2020-05-25 North East and Yorkshire 21
## 417 2020-05-26 North East and Yorkshire 21
## 418 2020-05-27 North East and Yorkshire 22
## 419 2020-05-28 North East and Yorkshire 20
## 420 2020-05-29 North East and Yorkshire 25
## 421 2020-05-30 North East and Yorkshire 20
## 422 2020-05-31 North East and Yorkshire 20
## 423 2020-06-01 North East and Yorkshire 16
## 424 2020-06-02 North East and Yorkshire 22
## 425 2020-06-03 North East and Yorkshire 22
## 426 2020-06-04 North East and Yorkshire 17
## 427 2020-06-05 North East and Yorkshire 17
## 428 2020-06-06 North East and Yorkshire 21
## 429 2020-06-07 North East and Yorkshire 13
## 430 2020-06-08 North East and Yorkshire 11
## 431 2020-06-09 North East and Yorkshire 11
## 432 2020-06-10 North East and Yorkshire 17
## 433 2020-06-11 North East and Yorkshire 7
## 434 2020-06-12 North East and Yorkshire 9
## 435 2020-06-13 North East and Yorkshire 10
## 436 2020-06-14 North East and Yorkshire 11
## 437 2020-06-15 North East and Yorkshire 8
## 438 2020-06-16 North East and Yorkshire 10
## 439 2020-06-17 North East and Yorkshire 5
## 440 2020-06-18 North East and Yorkshire 1
## 441 2020-03-01 North West 0
## 442 2020-03-02 North West 0
## 443 2020-03-03 North West 0
## 444 2020-03-04 North West 0
## 445 2020-03-05 North West 1
## 446 2020-03-06 North West 0
## 447 2020-03-07 North West 0
## 448 2020-03-08 North West 1
## 449 2020-03-09 North West 0
## 450 2020-03-10 North West 0
## 451 2020-03-11 North West 0
## 452 2020-03-12 North West 2
## 453 2020-03-13 North West 3
## 454 2020-03-14 North West 1
## 455 2020-03-15 North West 4
## 456 2020-03-16 North West 2
## 457 2020-03-17 North West 4
## 458 2020-03-18 North West 6
## 459 2020-03-19 North West 7
## 460 2020-03-20 North West 10
## 461 2020-03-21 North West 11
## 462 2020-03-22 North West 13
## 463 2020-03-23 North West 15
## 464 2020-03-24 North West 21
## 465 2020-03-25 North West 21
## 466 2020-03-26 North West 29
## 467 2020-03-27 North West 35
## 468 2020-03-28 North West 28
## 469 2020-03-29 North West 46
## 470 2020-03-30 North West 67
## 471 2020-03-31 North West 52
## 472 2020-04-01 North West 86
## 473 2020-04-02 North West 96
## 474 2020-04-03 North West 95
## 475 2020-04-04 North West 98
## 476 2020-04-05 North West 102
## 477 2020-04-06 North West 100
## 478 2020-04-07 North West 135
## 479 2020-04-08 North West 127
## 480 2020-04-09 North West 119
## 481 2020-04-10 North West 117
## 482 2020-04-11 North West 138
## 483 2020-04-12 North West 125
## 484 2020-04-13 North West 129
## 485 2020-04-14 North West 131
## 486 2020-04-15 North West 114
## 487 2020-04-16 North West 135
## 488 2020-04-17 North West 98
## 489 2020-04-18 North West 113
## 490 2020-04-19 North West 71
## 491 2020-04-20 North West 83
## 492 2020-04-21 North West 76
## 493 2020-04-22 North West 86
## 494 2020-04-23 North West 85
## 495 2020-04-24 North West 66
## 496 2020-04-25 North West 65
## 497 2020-04-26 North West 55
## 498 2020-04-27 North West 54
## 499 2020-04-28 North West 57
## 500 2020-04-29 North West 62
## 501 2020-04-30 North West 59
## 502 2020-05-01 North West 45
## 503 2020-05-02 North West 56
## 504 2020-05-03 North West 55
## 505 2020-05-04 North West 48
## 506 2020-05-05 North West 48
## 507 2020-05-06 North West 44
## 508 2020-05-07 North West 49
## 509 2020-05-08 North West 42
## 510 2020-05-09 North West 30
## 511 2020-05-10 North West 41
## 512 2020-05-11 North West 34
## 513 2020-05-12 North West 38
## 514 2020-05-13 North West 25
## 515 2020-05-14 North West 26
## 516 2020-05-15 North West 33
## 517 2020-05-16 North West 32
## 518 2020-05-17 North West 24
## 519 2020-05-18 North West 31
## 520 2020-05-19 North West 35
## 521 2020-05-20 North West 27
## 522 2020-05-21 North West 26
## 523 2020-05-22 North West 26
## 524 2020-05-23 North West 31
## 525 2020-05-24 North West 26
## 526 2020-05-25 North West 31
## 527 2020-05-26 North West 27
## 528 2020-05-27 North West 27
## 529 2020-05-28 North West 28
## 530 2020-05-29 North West 20
## 531 2020-05-30 North West 17
## 532 2020-05-31 North West 13
## 533 2020-06-01 North West 12
## 534 2020-06-02 North West 27
## 535 2020-06-03 North West 21
## 536 2020-06-04 North West 22
## 537 2020-06-05 North West 15
## 538 2020-06-06 North West 23
## 539 2020-06-07 North West 19
## 540 2020-06-08 North West 20
## 541 2020-06-09 North West 15
## 542 2020-06-10 North West 14
## 543 2020-06-11 North West 16
## 544 2020-06-12 North West 7
## 545 2020-06-13 North West 8
## 546 2020-06-14 North West 15
## 547 2020-06-15 North West 14
## 548 2020-06-16 North West 8
## 549 2020-06-17 North West 9
## 550 2020-06-18 North West 0
## 551 2020-03-01 South East 0
## 552 2020-03-02 South East 0
## 553 2020-03-03 South East 1
## 554 2020-03-04 South East 0
## 555 2020-03-05 South East 1
## 556 2020-03-06 South East 0
## 557 2020-03-07 South East 0
## 558 2020-03-08 South East 1
## 559 2020-03-09 South East 1
## 560 2020-03-10 South East 1
## 561 2020-03-11 South East 1
## 562 2020-03-12 South East 0
## 563 2020-03-13 South East 1
## 564 2020-03-14 South East 1
## 565 2020-03-15 South East 5
## 566 2020-03-16 South East 8
## 567 2020-03-17 South East 7
## 568 2020-03-18 South East 10
## 569 2020-03-19 South East 9
## 570 2020-03-20 South East 13
## 571 2020-03-21 South East 7
## 572 2020-03-22 South East 25
## 573 2020-03-23 South East 20
## 574 2020-03-24 South East 22
## 575 2020-03-25 South East 29
## 576 2020-03-26 South East 35
## 577 2020-03-27 South East 34
## 578 2020-03-28 South East 36
## 579 2020-03-29 South East 55
## 580 2020-03-30 South East 58
## 581 2020-03-31 South East 65
## 582 2020-04-01 South East 66
## 583 2020-04-02 South East 55
## 584 2020-04-03 South East 72
## 585 2020-04-04 South East 80
## 586 2020-04-05 South East 82
## 587 2020-04-06 South East 88
## 588 2020-04-07 South East 100
## 589 2020-04-08 South East 83
## 590 2020-04-09 South East 104
## 591 2020-04-10 South East 88
## 592 2020-04-11 South East 88
## 593 2020-04-12 South East 88
## 594 2020-04-13 South East 84
## 595 2020-04-14 South East 65
## 596 2020-04-15 South East 72
## 597 2020-04-16 South East 56
## 598 2020-04-17 South East 86
## 599 2020-04-18 South East 57
## 600 2020-04-19 South East 70
## 601 2020-04-20 South East 87
## 602 2020-04-21 South East 50
## 603 2020-04-22 South East 54
## 604 2020-04-23 South East 57
## 605 2020-04-24 South East 64
## 606 2020-04-25 South East 51
## 607 2020-04-26 South East 51
## 608 2020-04-27 South East 40
## 609 2020-04-28 South East 40
## 610 2020-04-29 South East 47
## 611 2020-04-30 South East 29
## 612 2020-05-01 South East 37
## 613 2020-05-02 South East 36
## 614 2020-05-03 South East 17
## 615 2020-05-04 South East 35
## 616 2020-05-05 South East 29
## 617 2020-05-06 South East 25
## 618 2020-05-07 South East 27
## 619 2020-05-08 South East 26
## 620 2020-05-09 South East 28
## 621 2020-05-10 South East 19
## 622 2020-05-11 South East 25
## 623 2020-05-12 South East 27
## 624 2020-05-13 South East 18
## 625 2020-05-14 South East 32
## 626 2020-05-15 South East 24
## 627 2020-05-16 South East 22
## 628 2020-05-17 South East 18
## 629 2020-05-18 South East 22
## 630 2020-05-19 South East 12
## 631 2020-05-20 South East 22
## 632 2020-05-21 South East 14
## 633 2020-05-22 South East 17
## 634 2020-05-23 South East 21
## 635 2020-05-24 South East 17
## 636 2020-05-25 South East 13
## 637 2020-05-26 South East 19
## 638 2020-05-27 South East 18
## 639 2020-05-28 South East 12
## 640 2020-05-29 South East 21
## 641 2020-05-30 South East 8
## 642 2020-05-31 South East 10
## 643 2020-06-01 South East 11
## 644 2020-06-02 South East 13
## 645 2020-06-03 South East 17
## 646 2020-06-04 South East 11
## 647 2020-06-05 South East 11
## 648 2020-06-06 South East 10
## 649 2020-06-07 South East 11
## 650 2020-06-08 South East 7
## 651 2020-06-09 South East 9
## 652 2020-06-10 South East 10
## 653 2020-06-11 South East 5
## 654 2020-06-12 South East 5
## 655 2020-06-13 South East 4
## 656 2020-06-14 South East 6
## 657 2020-06-15 South East 7
## 658 2020-06-16 South East 9
## 659 2020-06-17 South East 6
## 660 2020-06-18 South East 0
## 661 2020-03-01 South West 0
## 662 2020-03-02 South West 0
## 663 2020-03-03 South West 0
## 664 2020-03-04 South West 0
## 665 2020-03-05 South West 0
## 666 2020-03-06 South West 0
## 667 2020-03-07 South West 0
## 668 2020-03-08 South West 0
## 669 2020-03-09 South West 0
## 670 2020-03-10 South West 0
## 671 2020-03-11 South West 1
## 672 2020-03-12 South West 0
## 673 2020-03-13 South West 0
## 674 2020-03-14 South West 1
## 675 2020-03-15 South West 0
## 676 2020-03-16 South West 0
## 677 2020-03-17 South West 2
## 678 2020-03-18 South West 2
## 679 2020-03-19 South West 4
## 680 2020-03-20 South West 3
## 681 2020-03-21 South West 6
## 682 2020-03-22 South West 7
## 683 2020-03-23 South West 8
## 684 2020-03-24 South West 7
## 685 2020-03-25 South West 9
## 686 2020-03-26 South West 11
## 687 2020-03-27 South West 13
## 688 2020-03-28 South West 21
## 689 2020-03-29 South West 18
## 690 2020-03-30 South West 23
## 691 2020-03-31 South West 23
## 692 2020-04-01 South West 22
## 693 2020-04-02 South West 23
## 694 2020-04-03 South West 30
## 695 2020-04-04 South West 42
## 696 2020-04-05 South West 32
## 697 2020-04-06 South West 34
## 698 2020-04-07 South West 39
## 699 2020-04-08 South West 47
## 700 2020-04-09 South West 24
## 701 2020-04-10 South West 46
## 702 2020-04-11 South West 43
## 703 2020-04-12 South West 23
## 704 2020-04-13 South West 27
## 705 2020-04-14 South West 24
## 706 2020-04-15 South West 32
## 707 2020-04-16 South West 29
## 708 2020-04-17 South West 33
## 709 2020-04-18 South West 25
## 710 2020-04-19 South West 31
## 711 2020-04-20 South West 26
## 712 2020-04-21 South West 26
## 713 2020-04-22 South West 23
## 714 2020-04-23 South West 17
## 715 2020-04-24 South West 19
## 716 2020-04-25 South West 15
## 717 2020-04-26 South West 27
## 718 2020-04-27 South West 13
## 719 2020-04-28 South West 17
## 720 2020-04-29 South West 15
## 721 2020-04-30 South West 26
## 722 2020-05-01 South West 6
## 723 2020-05-02 South West 7
## 724 2020-05-03 South West 10
## 725 2020-05-04 South West 17
## 726 2020-05-05 South West 14
## 727 2020-05-06 South West 19
## 728 2020-05-07 South West 16
## 729 2020-05-08 South West 6
## 730 2020-05-09 South West 11
## 731 2020-05-10 South West 5
## 732 2020-05-11 South West 8
## 733 2020-05-12 South West 7
## 734 2020-05-13 South West 7
## 735 2020-05-14 South West 6
## 736 2020-05-15 South West 4
## 737 2020-05-16 South West 4
## 738 2020-05-17 South West 6
## 739 2020-05-18 South West 4
## 740 2020-05-19 South West 6
## 741 2020-05-20 South West 1
## 742 2020-05-21 South West 9
## 743 2020-05-22 South West 6
## 744 2020-05-23 South West 6
## 745 2020-05-24 South West 3
## 746 2020-05-25 South West 8
## 747 2020-05-26 South West 11
## 748 2020-05-27 South West 5
## 749 2020-05-28 South West 10
## 750 2020-05-29 South West 7
## 751 2020-05-30 South West 3
## 752 2020-05-31 South West 2
## 753 2020-06-01 South West 7
## 754 2020-06-02 South West 2
## 755 2020-06-03 South West 5
## 756 2020-06-04 South West 2
## 757 2020-06-05 South West 2
## 758 2020-06-06 South West 1
## 759 2020-06-07 South West 3
## 760 2020-06-08 South West 3
## 761 2020-06-09 South West 0
## 762 2020-06-10 South West 0
## 763 2020-06-11 South West 2
## 764 2020-06-12 South West 2
## 765 2020-06-13 South West 2
## 766 2020-06-14 South West 0
## 767 2020-06-15 South West 1
## 768 2020-06-16 South West 1
## 769 2020-06-17 South West 0
## 770 2020-06-18 South West 01.5 Completion date
We extract the completion date from the NHS Pathways file timestamp:
The completion date of the NHS Pathways data is Thursday 18 Jun 2020.
1.6 Auxiliary functions
These are functions which will be used further in the analyses.
Function to estimate the generalised R-squared as the proportion of deviance explained by a given model:
## Function to calculate R2 for Poisson model
## not adjusted for model complexity but all models have the same DF here
Rsq <- function(x) {
1 - (x$deviance / x$null.deviance)
}Function to extract growth rates per region as well as halving times, and the associated 95% confidence intervals:
## function to extract the coefficients, find the level of the intercept,
## reconstruct the values of r, get confidence intervals
get_r <- function(model) {
## extract coefficients and conf int
out <- data.frame(r = coef(model)) %>%
rownames_to_column("var") %>%
cbind(confint(model)) %>%
filter(!grepl("day_of_week", var)) %>%
filter(grepl("day", var)) %>%
rename(lower_95 = "2.5 %",
upper_95 = "97.5 %") %>%
mutate(var = sub("day:", "", var))
## reconstruct values: intercept + region-coefficient
for (i in 2:nrow(out)) {
out[i, -1] <- out[1, -1] + out[i, -1]
}
## find the name of the intercept, restore regions names
out <- out %>%
mutate(nhs_region = model$xlevels$nhs_region) %>%
select(nhs_region, everything(), -var)
## find halving times
halving <- log(0.5) / out[,-1] %>%
rename(halving_t = r,
halving_t_lower_95 = lower_95,
halving_t_upper_95 = upper_95)
## set halving times with exclusion intervals to NA
no_halving <- out$lower_95 < 0 & out$upper_95 > 0
halving[no_halving, ] <- NA_real_
## return all data
cbind(out, halving)
}Functions used in the correlation analysis between NHS Pathways reports and deaths:
## Function to calculate Pearson's correlation between deaths and lagged
## reports. Note that `pearson` can be replaced with `spearman` for rank
## correlation.
getcor <- function(x, ndx) {
return(cor(x$deaths[ndx],
x$note_lag[ndx],
use = "complete.obs",
method = "pearson"))
}
## Catch if sample size throws an error
getcor2 <- possibly(getcor, otherwise = NA)
getboot <- function(x) {
result <- boot::boot.ci(boot::boot(x, getcor2, R = 1000),
type = "bca")
return(data.frame(n = sum(!is.na(x$note_lag) & !is.na(x$deaths)),
r = result$t0,
r_low = result$bca[4],
r_hi = result$bca[5]))
}Function to classify the day of the week into weekend, Monday, and the rest:
## Fn to add day of week
day_of_week <- function(df) {
df %>%
dplyr::mutate(day_of_week = lubridate::wday(date, label = TRUE)) %>%
dplyr::mutate(day_of_week = dplyr::case_when(
day_of_week %in% c("Sat", "Sun") ~ "weekend",
day_of_week %in% c("Mon") ~ "monday",
!(day_of_week %in% c("Sat", "Sun", "Mon")) ~ "rest_of_week"
) %>%
factor(levels = c("rest_of_week", "monday", "weekend")))
}Custom color palettes, color scales, and vectors of colors:
2 Temporal trends in NHS Pathways reports
2.1 Outline
We look for temporal patterns in COVID-19 related 111/999 calls and 111 online reports. Analyses are broken down by NHS region. We also look for estimates of recent growth rate and associated doubling / halving time.
2.2 Reports by region over time
tab_date_region_all <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
dth %>%
mutate(trusted = case_when(date_report < max(dth$date_report)-delay_max ~ "Y",
date_report >= max(dth$date_report)-delay_max ~ "N"),
value = "Deaths",
vline = max(dth$date_report)-delay_max-1,
lab = "Truncated for reporting delay",
lab_pos_x = vline + 10,
lab_pos_y = 150,
lab_col = "darkgrey") %>%
rename(date = date_report,
n = deaths) %>%
bind_rows(
mutate(tab_date_region_all, value = "Reports",
trusted = "Y",
vline = as.Date("2020-03-23"),
lab = "Start of UK lockdown",
lab_pos_x = vline - 8,
lab_pos_y = 30200,
lab_col = "black")
) %>%
mutate(value = factor(value, levels = c("Reports","Deaths"))) -> dths_reports
plot_dth_report <-
ggplot(dths_reports, aes(date, n, colour = nhs_region)) +
# Add main points and lines, coloured by region and fade out deaths for excluded period
geom_point(aes(alpha = trusted)) +
geom_line(alpha = 0.2) +
geom_smooth(method = "loess", span = .5, color = "black") +
scale_colour_manual("", values = pal) +
scale_alpha_manual(values = c(0.3,1)) +
guides(alpha = F) +
# Add vertical markers for important dates with labels - different for each facet
ggnewscale::new_scale_colour() +
geom_vline(aes(xintercept = vline, col = value), lty = "solid") +
geom_text(aes(x = lab_pos_x, y = lab_pos_y, label = lab, col = value), size = 3) +
scale_colour_manual("",values = c("black","darkgrey"), guide = F) +
# Facet by deaths and reports
facet_grid(rows = vars(value), scales = "free_y", switch = "y") +
# Other formatting
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",strip.placement = "outside") +
rotate_x +
labs(x = NULL,
y = NULL)
plot_dth_report
2.3 Reports by region by age over time
We plot the number of 111/999 calls and 111 online reports by age, and the proportion of 111/999 calls and 111 online reports by age. In the second graph, the vertical lines indicate the proportion of individuals residing in the corresponding NHS region who belong to the corresponding age group.
tab_date_region_age_all <- x %>%
filter(!is.na(nhs_region),
age != "missing") %>%
group_by(date, nhs_region, age) %>%
summarise(n = sum(count))
tab_date_region_age_all %>%
ggplot(aes(x = date, y = n, fill = age)) +
geom_col(position = "stack") +
scale_fill_manual(values = age.pal) +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90, hjust = 1)) +
guides(fill = guide_legend(title = "Age", ncol = 3)) +
labs(x = NULL,
y = "Total daily reports by age") +
facet_wrap(~ nhs_region, ncol = 4)
tab_date_region_age_all <- tab_date_region_age_all %>%
group_by(date, nhs_region) %>%
summarise(tot = sum(n)) %>%
left_join(tab_date_region_age_all, by = c("date", "nhs_region")) %>%
mutate(prop_n = n/tot)
tab_date_region_age_all %>%
ggplot(aes(x = date, y = prop_n, color = age)) +
scale_color_manual(values = age.pal) +
geom_line() +
geom_point() +
geom_hline(data = nhs_region_pop, aes(yintercept = value, color = variable)) +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90, hjust = 1)) +
guides(color = guide_legend(title = "Age", ncol = 3)) +
labs(x = NULL,
y = "Proportion of daily reports by age") +
facet_wrap(~ nhs_region, ncol = 4)
2.4 Moving window model
We fit quasi-Poisson GLMs for 14-day windows to get growth rates over time.
## set moving time window (1/2/3 weeks)
w <- 14
# create empty df
r_all_sliding <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding <- bind_rows(r_all_sliding, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding <- r_all_sliding %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))We examine the evolution of the growth rate by region over time.
# plot
plot_growth <-
r_all_sliding %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)From the growth rate, we derive R and examine its value through time.
# plot
plot_R <-
r_all_sliding %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
rotate_x +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
# strip.text.x = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "",
override.aes = list(fill = NA)),
fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))
2.5 Moving window model - stratified by age
We repeat the above analysis, where we fit quasi-Poisson GLMs for 14-day windows to get growth rates over time, but apply this to each age group separately (0-18, 19-69, 70-120 years old).
We first run the analysis for 0-18 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_0_18 <- NULL
## make data for model
x_model_all_moving_0_18 <- x %>%
filter(!is.na(nhs_region),
age == "0-18") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_0_18$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_0_18 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_0_18 <- bind_rows(r_all_sliding_0_18, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_0_18 <- r_all_sliding_0_18 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_0_18 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)"
) +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_0_18 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)"
) +
scale_colour_manual(values = pal)
R <- r_all_sliding_0_18 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_0_18 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_0_18 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Then, we run the analysis for 19-69 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_19_69 <- NULL
## make data for model
x_model_all_moving_19_69 <- x %>%
filter(!is.na(nhs_region),
age == "19-69") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_19_69$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_19_69 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_19_69 <- bind_rows(r_all_sliding_19_69, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_19_69 <- r_all_sliding_19_69 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_19_69 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_19_69 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)"
) +
scale_colour_manual(values = pal)
R <- r_all_sliding_19_69 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_19_69 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_19_69 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Finally, we run the analysis for 70-120 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_70_120 <- NULL
## make data for model
x_model_all_moving_70_120 <- x %>%
filter(!is.na(nhs_region),
age == "70-120") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_70_120$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_70_120 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_70_120 <- bind_rows(r_all_sliding_70_120, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_70_120 <- r_all_sliding_70_120 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_70_120 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)"
) +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_70_120 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_70_120 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_70_120 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_70_120 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)"))) We combine the estimated growth rates and effective reproduction numbers into a single figure.
ggpubr::ggarrange(fig2_3_0_18,
fig2_3_19_69,
fig2_3_70_120,
nrow = 3,
labels = "AUTO",
common.legend = TRUE,
legend = "bottom",
align = "hv") 
3 Comparison with deaths time series
3.1 Outline
We want to explore the correlation between NHS Pathways reports and deaths, and assess the potential for reports to be used as an early warning system for disease resurgence.
Death data are publically available. We truncate the time series to avoid bias from reporting delay - we assume a conservative delay of three weeks.
3.2 Lagged correlation
We calculate Pearson’s correlation coefficient between deaths and NHS Pathways notifications using different lags. Confidence intervals are obtained using bootstrap. Note that results were also confirmed using Spearman’s rank correlation.
First we join the NHS Pathways and death data, and aggregate over all England:
## truncate death data for reporting delay
trunc_date <- max(dth$date_report) - delay_max
dth_trunc <- dth %>%
rename(date = date_report) %>%
filter(date <= trunc_date)
## join with notification data
all_data <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(count = sum(count, na.rm = T)) %>%
ungroup %>%
inner_join(dth_trunc,
by = c("date","nhs_region"))
all_tot <- all_data %>%
group_by(date) %>%
summarise(count = sum(count, na.rm = TRUE),
deaths = sum(deaths, na.rm = TRUE)) We calculate correlation with lagged NHS Pathways reports from 0 to 30 days behind deaths:
## Calculate all correlations + bootstrap CIs
lag_cor <- data.frame()
for (i in 0:30) {
## lag reports
summary <- all_tot %>%
mutate(note_lag = lag(count, i)) %>%
## calculate rank correlation and bootstrap CI
getboot(.) %>%
mutate(lag = i)
lag_cor <- bind_rows(lag_cor, summary)
}
cor_vs_lag <- ggplot(lag_cor, aes(lag, r)) +
theme_bw() +
geom_ribbon(aes(ymin = r_low, ymax = r_hi), alpha = 0.2) +
geom_hline(yintercept = 0, lty = "longdash") +
geom_point() +
geom_line() +
labs(x = "Lag between NHS pathways and death data (days)",
y = "Pearson's correlation") +
large_txt
cor_vs_lag
This analysis suggests that the best lag is 23 days. We then compare and plot the number of deaths reported against the number of NHS Pathways reports lagged by 23 days.
all_tot <- all_tot %>%
rename(date_death = date) %>%
mutate(note_lag = lag(count, lag_cor$lag[l_opt]),
note_lag_c = (note_lag - mean(note_lag, na.rm = T)),
date_note = lag(date_death,16))
lag_mod <- glm(deaths ~ note_lag, data = all_tot, family = "quasipoisson")
summary(lag_mod)
##
## Call:
## glm(formula = deaths ~ note_lag, family = "quasipoisson", data = all_tot)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -9.3364 -2.7683 -0.1921 2.9300 4.9242
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.953e+00 5.234e-02 94.63 <2e-16 ***
## note_lag 1.157e-05 5.228e-07 22.13 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 10.81681)
##
## Null deviance: 5665.18 on 48 degrees of freedom
## Residual deviance: 522.65 on 47 degrees of freedom
## (23 observations deleted due to missingness)
## AIC: NA
##
## Number of Fisher Scoring iterations: 4
exp(coefficients(lag_mod))
## (Intercept) note_lag
## 141.615294 1.000012
exp(confint(lag_mod))
## 2.5 % 97.5 %
## (Intercept) 127.664875 156.743494
## note_lag 1.000011 1.000013
Rsq(lag_mod)
## [1] 0.9077432
mod_fit <- as.data.frame(predict(lag_mod, type = "link", se.fit = TRUE)[1:2])
all_tot_pred <-
all_tot %>%
filter(!is.na(note_lag)) %>%
mutate(pred = mod_fit$fit,
pred.se = mod_fit$se.fit,
low = exp(pred - 1.96*pred.se),
hi = exp(pred + 1.96*pred.se))
glm_fit <- all_tot_pred %>%
filter(!is.na(note_lag)) %>%
ggplot(aes(x = note_lag, y = deaths)) +
geom_point() +
geom_line(aes(y = exp(pred))) +
geom_ribbon(aes(ymin = low, ymax = hi), alpha = 0.3, col = "grey") +
theme_bw() +
labs(y = "Daily number of\ndeaths reported",
x = "Daily number of NHS Pathways reports") +
large_txt
glm_fit
4 Supplementary figures
4.1 Serial interval distribution
This is a comparison of gamma versus lognormal distribution for the serial interval used to convert r to R in our analysis. Both distributions are parameterised with mean 4.7 and standard deviation 2.9.
SI_param <- epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
SI_distribution2 <- distcrete::distcrete("lnorm", interval = 1,
meanlog = log(4.7),
sdlog = log(2.9), w = 0.5)
SI_dist1 <- data.frame(x = SI_distribution$r(1e5))
SI_dist1 <- count(SI_dist1, x) %>%
ggplot() +
geom_col(aes(x = x, y = n)) +
labs(x = "Serial interval (days)", y = "Frequency") +
scale_x_continuous(breaks = seq(0, 30, 5)) +
theme_bw()
SI_dist2 <- data.frame(x = SI_distribution2$r(1e5))
SI_dist2 <- count(SI_dist2, x) %>%
ggplot() +
geom_col(aes(x = x, y = n)) +
labs(x = "Serial interval (days)", y = "Frequency") +
scale_x_continuous(breaks = seq(0, 200, 20), limits = c(0, 200)) +
theme_bw()
ggpubr::ggarrange(SI_dist1,
SI_dist2,
nrow = 1,
labels = "AUTO") 
4.2 Sensitivity analysis - 7 or 21 days moving window
We reproduce the window analysis with either a 7 or 21 days window for sensitivity purposes.
First with the 7 days window:
## set moving time window (1/2/3 weeks)
w <- 7
# create empty df
r_all_sliding_7days <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_7days <- bind_rows(r_all_sliding_7days, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding_7days <- r_all_sliding_7days %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_7days %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)plot_R <- r_all_sliding_7days %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_7days %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_7days %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R_7 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Then with the 21 days window:
## set moving time window (1/2/3 weeks)
w <- 21
# create empty df
r_all_sliding_21days <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_21days <- bind_rows(r_all_sliding_21days, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding_21days <- r_all_sliding_21days %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_21days %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_21days %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_21days %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_21days %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R_21 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))And we combine both outputs into a single plot:
ggpubr::ggarrange(r_R_7,
r_R_21,
nrow = 2,
labels = "AUTO",
common.legend = TRUE,
legend = "bottom") 
4.3 Correlation between NHS Pathways reports and deaths by NHS region
lag_cor_reg <- data.frame()
for (i in 0:30) {
summary <-
all_data %>%
group_by(nhs_region) %>%
mutate(note_lag = lag(count, i)) %>%
## calculate rank correlation and bootstrap CI for each region
group_modify(~getboot(.x)) %>%
mutate(lag = i)
lag_cor_reg <- bind_rows(lag_cor_reg, summary)
}
cor_vs_lag_reg <-
lag_cor_reg %>%
ggplot(aes(lag, r, col = nhs_region)) +
geom_hline(yintercept = 0, lty = "longdash") +
geom_ribbon(aes(ymin = r_low, ymax = r_hi, col = NULL, fill = nhs_region), alpha = 0.2) +
geom_point() +
geom_line() +
facet_wrap(~nhs_region) +
scale_color_manual(values = pal) +
scale_fill_manual(values = pal, guide = F) +
theme_bw() +
labs(x = "Lag between NHS pathways and death data (days)", y = "Pearson's correlation", col = "NHS region") +
theme(legend.position = "bottom") +
guides(color = guide_legend(override.aes = list(fill = NA)))
cor_vs_lag_reg
5 Export data
We save the tables created during our analysis:
if (!dir.exists("excel_tables")) {
dir.create("excel_tables")
}
## list all tables, and loop over export
tables_to_export <- c("r_all_sliding", "lag_cor")
for (e in tables_to_export) {
rio::export(get(e),
file.path("excel_tables",
paste0(e, ".xlsx")))
}
## also export result from regression on lagged data
rio::export(lag_mod, file.path("excel_tables", "lag_mod.rds"))6 System information
6.1 Outline
The following information documents the system on which the document was compiled.
6.2 System
This provides information on the operating system.
6.3 R environment
This provides information on the version of R used:
6.4 R packages
This provides information on the packages used:
sessionInfo()
## R version 3.6.3 (2020-02-29)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Catalina 10.15.5
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggnewscale_0.4.1 ggpubr_0.3.0 lubridate_1.7.9
## [4] chngpt_2020.5-21 cyphr_1.1.0 DT_0.13
## [7] kableExtra_1.1.0 janitor_2.0.1 remotes_2.1.1
## [10] projections_0.4.1 earlyR_0.0.1 epitrix_0.2.2
## [13] distcrete_1.0.3 incidence_1.7.1 rio_0.5.16
## [16] reshape2_1.4.4 rvest_0.3.5 xml2_1.3.2
## [19] linelist_0.0.40.9000 forcats_0.5.0 stringr_1.4.0
## [22] dplyr_1.0.0 purrr_0.3.4 readr_1.3.1
## [25] tidyr_1.1.0 tibble_3.0.1 ggplot2_3.3.2
## [28] tidyverse_1.3.0 here_0.1 reportfactory_0.0.5
##
## loaded via a namespace (and not attached):
## [1] colorspace_1.4-1 selectr_0.4-2 ggsignif_0.6.0 ellipsis_0.3.1
## [5] rprojroot_1.3-2 snakecase_0.11.0 fs_1.4.1 rstudioapi_0.11
## [9] farver_2.0.3 fansi_0.4.1 splines_3.6.3 knitr_1.28
## [13] jsonlite_1.6.1 broom_0.5.6 dbplyr_1.4.4 compiler_3.6.3
## [17] httr_1.4.1 backports_1.1.8 assertthat_0.2.1 Matrix_1.2-18
## [21] cli_2.0.2 htmltools_0.5.0 prettyunits_1.1.1 tools_3.6.3
## [25] gtable_0.3.0 glue_1.4.1 Rcpp_1.0.4.6 carData_3.0-4
## [29] cellranger_1.1.0 vctrs_0.3.1 nlme_3.1-144 matchmaker_0.1.1
## [33] crosstalk_1.1.0.1 xfun_0.14 ps_1.3.3 openxlsx_4.1.5
## [37] lifecycle_0.2.0 rstatix_0.6.0 MASS_7.3-51.5 scales_1.1.1
## [41] hms_0.5.3 sodium_1.1 yaml_2.2.1 curl_4.3
## [45] gridExtra_2.3 stringi_1.4.6 kyotil_2019.11-22 boot_1.3-24
## [49] pkgbuild_1.0.8 zip_2.0.4 rlang_0.4.6 pkgconfig_2.0.3
## [53] evaluate_0.14 lattice_0.20-38 labeling_0.3 htmlwidgets_1.5.1
## [57] cowplot_1.0.0 processx_3.4.2 tidyselect_1.1.0 plyr_1.8.6
## [61] magrittr_1.5 R6_2.4.1 generics_0.0.2 DBI_1.1.0
## [65] pillar_1.4.4 haven_2.3.1 foreign_0.8-75 withr_2.2.0
## [69] mgcv_1.8-31 survival_3.1-8 abind_1.4-5 modelr_0.1.8
## [73] crayon_1.3.4 car_3.0-8 utf8_1.1.4 rmarkdown_2.3
## [77] viridis_0.5.1 grid_3.6.3 readxl_1.3.1 data.table_1.12.8
## [81] blob_1.2.1 callr_3.4.3 reprex_0.3.0 digest_0.6.25
## [85] webshot_0.5.2 munsell_0.5.0 viridisLite_0.3.0